Observer-based synergetic adaptive neural network control for a class of discrete-time nonlinear systems with dead-zone

ABSTRACT

In this paper, the observer-based synergetic adaptive neural network control method is designed for a class of discrete-time systems with dead-zone. A macro-variable is introduced by a synergetic approach to control theory and neural networks are utilised to estimate unmeasured states and unknown functions in the system. Furthermore, by employing an adaptive design procedure and Lyapunov stability theory, the closed-loop system stability is guaranteed, and the desired system performance is achieved simultaneously. Finally, some simulation results are given to prove the validity of the developed control method.

KEYWORDS:

• Synergetic adaptive control
• discrete-time system
• neural network (NN)-based observer
• dead-zone

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was partially supported by the National Natural Science Foundation of China [61703051], and the PhD Start-up Fund of Liaoning Province (20170520124).

Notes on contributors

Shiyi Zhao

Shiyi Zhao received the B.S. degree in Mathematics and Applied Mathematics from Bohai University, Jinzhou, China, in 2016. She is currently pursuing the M.S. degree in Applied Mathematics with Bohai University, Jinzhou, China. Her current research interests include adaptive control for nonlinear systems, neural network control and finite-time control.

Hongjing Liang

Hongjing Liang received the B.S. degree in mathematics from Bohai University, Jinzhou, China, in 2009, the M.S. degree in fundamental mathematics and the Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2011 and 2016, respectively. He is currently with Bohai University. His current research interests include multiagent systems, complex systems, and output regulation.

Peihao Du

Peihao Du received the B.S. degree in mathematics and applied mathematics from Bohai University, Jinzhou, China, in 2016. He is currently pursuing the M.S. degree in Applied Mathematics with Bohai University, Jinzhou, China. His current research interests include fuzzy control, neural network control, and finite-time control for nonlinear systems.